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Network interfaces in most LAN computing devices are usually severely under-utilized, wasting energy while waiting for new packets to arrive. In this paper, we present two algorithms for opportunistically powering down unused network…
The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem…
An effective way to improve energy efficiency is to throttle hardware resources to meet a certain performance target, specified as a QoS constraint, associated with all applications running on a multicore system. Prior art has proposed…
This paper presents an extension to an existing instruction set architecture, which gains considerable reduction in power consumption. The reduction in power consumption is achieved through coding of the most commonly executed instructions…
In this paper, an operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed, and its performance under different task types and system loads is verified by experiments. Compared with the traditional…
The pressing demands of improving energy efficiency for high performance scientific computing have motivated a large body of software-controlled hard- ware solutions using Dynamic Voltage and Frequency Scaling (DVFS) that strategically…
In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art LLMs. By analyzing the working mechanism and implementation of 20,543 public-accessible LLMs, we observe a…
Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time, and much energy. Nowadays, many-core AI accelerators (e.g.,…
Heterogeneous multi-core architectures combine a few "host" cores, optimized for single-thread performance, with many small energy-efficient "accelerator" cores for data-parallel processing, on a single chip. Offloading a computation to the…
This study focuses on edge computing in dense millimeter wave vehicle-to-everything (V2X) networks. A control problem is formulated to minimize the energy consumption under delay constraint resulting from vehicle mobility. A tractable…
One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast [9]. On one hand, speed can be…
Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes of program performance issues are critical tasks to fully…
In today's Internet, mobile devices are connected to multiple access networks, e.g., WiFi/DSL and LTE. To take advantage of the networks' diverse paths characteristics (delay, bandwidth, and reliability) and aggregate bandwidth, we need…
In this work, we propose a novel power allocation mechanism which allows one to optimize the energy-efficiency of base stations operating in the downlink. The energy-efficiency refers to the amount of bits that can be transmitted by the…
This paper introduces a packet-based dual-rate control strategy to face time-varying network-induced delays, packet dropouts and packet disorder in a Networked Control System. Slow-rate sensing enables to achieve energy saving by reducing…
Multithreaded Multi-core processors are prevalent today and are used for solving some of the important problems in computing. Resource imbalance can negatively impact overall performance in such processors. Hence balanced resource…
A router is a device, in the computer networks, that is used to forward a data packet. Because of its function, the router becomes an important role in measuring the performance of computer networks. It needs to process the incoming data…
This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task…
For some applications, we need to deploy a network of sensors in working field to sense the environment and send collected data to a base station for processing; these sensors depend on non-rechargeable batteries, so the routing protocols…
Latency and energy consumption are key metrics in the performance of deep neural network (DNN) accelerators. A significant factor contributing to latency and energy is data transfers. One method to reduce transfers or data is reusing data…